Measuring University Students Satisfaction with Traditional Search Engines and Generative AI Tools as Information Sources

2026-01-01

arXiv.org

AI Summary

This study investigates differences in satisfaction among university students regarding traditional search engines and generative AI tools as academic information sources, along with the factors influencing these perceptions. Drawing on an online survey of students at U.S. higher education institutions, the research employs principal component analysis, K-means clustering, and regression modeling to systematically distinguish and quantify the latent constructs underlying satisfaction with each type of source. Two distinct user profiles emerge, with usage frequency identified as a key predictor of satisfaction. Overall, students express greater satisfaction with traditional search engines; however, international and undergraduate students report significantly higher satisfaction with generative AI tools, which they predominantly regard as complementary rather than substitutive resources for academic inquiry.

Technology Category

• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Humans and AI: User Experience and Usability
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

This study examines university students levels of satisfaction with generative artificial intelligence (AI) tools and traditional search engines as academic information sources. An electronic survey was distributed to students at U.S. universities in late fall 2025, with 236 valid responses received. In addition to demographic information about respondents, frequency of use and levels of satisfaction with both generative AI and traditional search engines were measured. Principal components analysis identified distinct constructs of satisfaction for each information source, while k-means cluster analysis revealed two primary student groups: those highly satisfied with search engines but dissatisfied with AI, and those moderately to highly satisfied with both. Regression analysis showed that frequency of use strongly predicts satisfaction, with international and undergraduate students reporting significantly higher satisfaction with AI tools than domestic and graduate students. Students generally expressed higher levels of satisfaction with traditional search engines over generative AI tools. Those who did prefer AI tools appear to see them more as a complementary source of information rather than a replacement for other sources. These findings stress evolving patterns of student information seeking and use behavior and offer meaningful insights for evaluating and integrating both traditional and AI-driven information sources within higher education.

Problem

• student satisfaction
• generative AI
• search engines
• information sources
• higher education

Innovation

• generative AI
• information seeking behavior
• student satisfaction
• cluster analysis
• principal components analysis